What is ETL (Extract, Transform, Load)?

Extract, Transform, Load (ETL) is is the process of gathering data from an unlimited number of sources, organizing it together, and centralizing it into a single repository.

The concept was first developed in the 1970s and has since been a trusted process for taking data out of its existing format, making changes to ready it for relocation, and placing it in its new database home. But far from a one-size-fits all science, smart ETL must be highly customized for each business’ needs. Below is a look at the three phases of ETL, how it applies to business, and popular tools for simplifying the process.

The Value of ETL

Today more than ever businesses gather data from disparate sources and use it to make decisions. The data warehouse concept gained popularity because it provides a central location for ingesting, sorting, and storing all this information and turning it into usable information called business intelligence.

ETL-based data warehouses became the standard for producing business intelligence that works interactively with various departments like marketing, sales, customer service, and other traditional team divisions so organizations can both respond to customers and gain better insight to their business.

ETL tools help create business intelligence by sorting, combining and providing access to data in three steps:

Staging — Think of staging as the docking bay area of an ETL-based data warehouse, where digital trucks haul in mountains of data from every supplier in your business intelligence empire. As the data comes in, it is sorted and processed based on previously defined rules, before moving on to the next part of the warehouse.

Data integration — At this stage, the data is combined and blended, handling exceptions and issues with the outlined rules you’ve provided. The data is prepared for presentation in a unified, easy-to-understand format.

Access Layers — As the name suggests, the access layers are the front-end interface options provided to users for reaching and interacting with the data you want them to have.

The business intelligence distilled from this cycle can be the critical insight you need in today’s high-speed market. Let’s take a deeper look at how the ETL process collects from many sources of data and turns it into constant news you can use.

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How the ETL Process Works

It’s important to remember that ETL is a process, not an action. The three ordered, defined steps must be addressed individually and sequentially.

Step 1: Extract

Though the goal of ETL is producing succinct business intelligence, the extraction phase is anything but concise. Raw data measured in megabytes (or more) must be mined from an array of sources, including:

Existing databases

Activity logs like network traffic, error reports, and more

Application performance and anomalies

Security events

Other transaction activity that must be reported for compliance

This is the phase wherein all those hypothetical trucks from the staging team work 24/7 extracting and hauling this massive amount of information to an ETL-based data warehouse. But because the extraction process assumes that more is better for informational purposes, extraction-phase data arrives in petabytes that would be be unmanageable without the next step in the ETL process.

Step 2: Transformation

Most users will never be aware of it, but the transformation stage of ETL is where the most critical work takes place. The tons of data mined and delivered through the extraction process must be reduced, refined, sorted, and polished before they become valuable.

Transformation is achieved through a series of rules and regulations you outline. Fundamental goals during this stage should include:

Standardization — Define what data will be in play, how it will be formatted and stored, and other baseline considerations that will define every steps that follows.

Deduplication — Excluding and discarding redundant data will massively reduce the working dataset.

Verification — Run automated checks to compare similar information like transaction times and access records. Verification tasks further prune unusable data and can red-flag anomalies in your network.

Sorting — Modern data warehouses maximize efficiency by grouping and storing items like raw data, audio, multimedia, and other objects in categories. Transformation rules will determine how each data piece is classified and where it will go next.

Other tasks you define and set to automatically execute.

These transformation steps pare down what was once a mass of unusable material into a data product you can present in the final step of ETL, the load phase.

Step 3: Load

Loading this extracted, transformed data into its new home is the last stage of the ETL process. Running as a series of automated procedures, data loads in a variety of ways. Depending on storage and throughput considerations, the loading procedure can run in two main ways:

Full Loading — In an ETL full loading scenario, everything that comes from the transformation assembly line goes into new, unique records in the data warehouse. Though there may be times this is useful for research purposes, full loading produces data sets that grow exponentially and can quickly become difficult to maintain.

Imagine that a small, corner grocery with limited space orders three dozen apples for the day it opens. In a full load delivery scenario, 36 apples would arrive each and every day, regardless of important metrics like apples sold and available square footage. In other words, full loading limits the agility of your ETL process..

Incremental Loading — A less comprehensive but more manageable approach is incremental loading. Incremental loading compares incoming data with what’s already on hand, and only produces additional records if new and unique information is found. This architecture allows smaller, less expensive data warehouses to maintain and manage business intelligence.

The load choice and rules defining it are critical, as they will inform what data is available to users and how they can work with it.

Once the decisions and design are in place, executing a lone ETL cycle or a scheduled series of them is as simple as executing a task from a command line or GUI interface.

Historic ETL vs Modern Data Practices

Together the steps of ETL have provided the blueprint for blending, storing, and accessing massive amounts of data. But will we always use ETL?

The popularization of relational business databases in the 1980s brought with it a need to aggregate data. With that came the inevitable desire to duplicate and share it. Tools were developed to build the first ETL warehouses and create business intelligence within and sometimes between industries like:

Government

Scientific research

Education

The military

Medical services

Big business and more

The tools evolved for decades, but the basic ETL process stayed largely intact. New technologies and growing data sets, however, are straining standard ETL practices beyond what they are likely to bear in two ways.

First, data begets more data, until the gap between loading and analyzing shrinks toward real-time. Secondly, as a result of increasing data stores, cloud technologies have changed the ways data is moved and stored.

The Challenges of Cloud ETL

Outsourcing — Turning to a trusted partner eliminates ETL headaches and provides state-of-the-art monitoring insights for almost any network structure. Some companies worry that the cost of data warehousing solutions may be a barrier, but there are free, open source ETL tools available that get the job done..

Tool modernization — For many businesses, a balance between new supported services for emerging techs like continuous delivery and legacy ETL is achievable by selecting and using the right tools.

Before overhauling an ETL process, or eliminating it altogether, work with your teams to develop a comprehensive picture of challenges and ETL software solutions that may be available for overcoming them.

ETL Tools: Do I Need One? How Do I Choose One?

A lot of IT Managers will, rightly so, start by asking if hand coding isn’t a better option than investing in another tool. There are some very specific situations in which custom coding makes more sense, but by and large, the right tool will create better results with a smaller overall investment.

Still trying to decide whether to use a hand-coding or tool-based approach? Our CEO has nine questions that will help you diagnose your situation and make the best choice.

Whatever your ETL big picture looks like, you can find established, trusted frameworks for creating it. Though many providers claim to offer complete services, look for partners who provide these features to simplify your ETL challenges.

Easy integration — ETL tools should be able to operate natively and configure easily, preferably with a GUI interface to make visualizing your environment simpler.

Scalability — As outlined above, modern environments can gather and store data at amazing rates, and sometimes demand will be higher than at others. A self-scaling ETL framework will save time, money, and endless headaches.

Open-source — Today’s tech shops are ever-changing environments, and there is no all-purpose fix for all things ETL-related. For this reason open-source architectural approaches, which harness the creativity and troubleshooting of thousands of developers across the world, improve your odds of great customized performance and rapid troubleshooting.

Cloud compatibility — Big data is getting bigger and systems are becoming more integrated. Staying competitive in almost any industry depends on the kind of access, storage, and integration that is only available from an organized, powerful cloud-based platform.

Every bolt can be tightened with the right wrench. One secret to modern ETL success is researching, testing, and deploying the right wrench for your environmental needs.

Getting Started with ETL

Since we have learned to aggregate and utilize digital data, the ETL process and the tools that support it have been integral parts of moving, blending, and putting information to work in the form of business intelligence. Today more than ever data is the bloodline for any healthy business, so make sure you’ve got the right approach, design, framework, and partners to succeed!